New AI Approaches and Algorithms for Medical Imaging Problems and Applications

Speaker: Professor Danny Ziyi Chen
Department of Computer Science and Engineering
University of Notre Dame
Title: "New AI Approaches and Algorithms for Medical Imaging
Problems and Applications"
Date: Monday, 19 November 2018
Time: 4:00pm - 5:00pm
Venue: Lecture Theater F (near lift 25/26), HKUST
Abstract:
Computer technology plays a crucial role in modern medicine, healthcare,
and life sciences, especially in medical imaging, human genome studies,
clinical diagnosis and prognosis, treatment planning and optimization,
treatment response monitoring and evaluation, and medical data management
and analysis. As computer technology rapidly evolves, computer science
solutions will inevitably become an integral part of modern medicine and
healthcare. Computational research and applications on modeling,
formulating, solving, and analyzing core problems in modern medicine and
healthcare are not only critical, but are actually indispensable.
Recently emerging deep learning (DL) techniques have achieved remarkably
high quality results for many computer vision tasks, such as image
classification, object detection, and semantic segmentation, largely
outperforming traditional image processing methods. In this talk, we
present new approaches based on DL techniques for solving a set of medical
imaging problems, such as segmentation and analysis of glial cells,
analysis of the relations between glial cells and brain tumors,
segmentation of neuron cells, new training strategies for deep learning
using sparse annotated medical image data, etc. We develop new deep
learning models, based on fully convolutional networks (FCN), recurrent
neural networks (RNN), and active learning, to effectively tackle the
target medical imaging problems. Further, we show that simply applying DL
techniques alone is often insufficient to solve medical imaging problems.
Hence, we propose new methods to complement and work with DL techniques.
For example, we devise a new cell cutting method based on k-terminal cut
in geometric graphs, which complements the voxel-level segmentation of FCN
to produce instance-level segmentation of 3D glial cells. We combine a set
of FCNs with an approximation algorithm for the maximum k-set cover
problem to form a new training strategy that utilizes significantly less
annotation data. A key point we make is that DL is often used as just one
main component in our approaches, which is complemented by other main
components and strategies, in order to achieve the best possible
solutions. We also show experimental data and results to illustrate the
practical applications of our new DL approaches.
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Biographpy:
Dr. Danny Ziyi Chen received the B.S. degrees in Computer Science
and in Mathematics from the University of San Francisco, California, USA
in 1985, and the M.S. and Ph.D. degrees in Computer Science from Purdue
University, West Lafayette, Indiana, USA in 1988 and 1992, respectively.
He has been on the faculty of the Department of Computer Science and
Engineering, the University of Notre Dame, Indiana, USA since 1992, and is
currently a Professor with tenure. Dr. Chen's main research interests are
in computational biomedicine, biomedical imaging, computational geometry,
algorithms and data structures, machine learning, data mining, and VLSI.
He has published over 130 journal papers and 210 peer-reviewed conference
papers in these areas, and holds 5 US patents for technology development
in computer science and engineering and biomedical applications. He
received the CAREER Award of the US National Science Foundation (NSF) in
1996, a Laureate Award in the 2011 Computerworld Honors Program for
developing "Arc-Modulated Radiation Therapy" (a new radiation cancer
treatment approach), and the 2017 PNAS Cozzarelli Prize of the US National
Academy of Sciences. He is a Fellow of IEEE and a Distinguished Scientist
of ACM.